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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Chee-Pun Ooi
Multimedia University
Computer Science & IT Electrical & Electronics Engineering

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Anomaly detection on in-home activities data based on time interval Soon-Chang Poh; Yi-Fei Tan; Soon-Nyean Cheong; Chee-Pun Ooi; Wooi-Haw Tan
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp778-785

Abstract

The world population of the elderly is expected to have a continuous growth and the number of elderly living in solitude is also expected to increase in the coming years. As our health decline with age, early detection of possible deterioration in health becomes important. Behavioral changes in in-home activities can be used as an indicator of health decline. For example, changes in routine of in-home activities. Past research mainly focused on detecting anomalies in routine of each type of in-home activities individually. In this paper, an anomaly detection model to detect changes in routine of in-home activities collectively for a day is proposed. The experiment was evaluated with an existing public dataset. The experimental results demonstrated that the anomaly detection model performed well on unseen testing data with an accuracy of 94.44%.
FPGA-based embedded architecture for IoT home automation application Chee-Pun Ooi; Wooi-Haw Tan; Soon-Nyean Cheong; Yee-Lien Lee; V. M. Baskaran; Yeong-Liang Low
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 2: May 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i2.pp646-652

Abstract

An Internet of Things (IoT) FPGA-based home hub to automate control operations in a home environment was designed and built. The proposed system uses an FPGA home hub as its local analytic engine with an IoT platform to store the sensory data. The FPGA was programmed in Verilog HDL using Quartus II provided by Altera. The WiFi capability of the FPGA was extended through an ESP8266 chip to ease the interfacing with various sensors connected to it. The system can be configured and monitored through a web application coded in JavaScript. Various test cases were carried out on the implemented system at Multimedia University (MMU) Digital Home Lab. The results verified the functionality of the system in triggering real home appliances (i.e. air conditioning unit and lighting) based on multiple sensor nodes without conflicting each other. The ability to allow user to configure the control rules based on the sensory data via web interface hosted using ThingSpeak Plugins is also presented and demonstrated in this project. The base design is utilizing Altera Cyclone IV EP4CE22F17C6N FPGA with 153 I/O pins, which is highly scalable and adaptable to the requirements of home environments. This shows promising application of FPGA in supporting scalable IoT home automation system.
Human activity recognition with self-attention Yi-Fei Tan; Soon-Chang Poh; Chee-Pun Ooi; Wooi-Haw Tan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2023-2029

Abstract

In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.
Facial emotion recognition using deep learning detector and classifier Ng Chin Kit; Chee-Pun Ooi; Wooi Haw Tan; Yi-Fei Tan; Soon-Nyean Cheong
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3375-3383

Abstract

Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyper-parameters, achieving an overall accuracy of 86.42% on the testing video dataset.
Co-Authors Ng Chin Kit Soon-Chang Poh Soon-Nyean Cheong V. M. Baskaran Wooi Haw Tan Wooi-Haw Tan Yee-Lien Lee Yeong-Liang Low Yi-Fei Tan